Collaborative Image Retrieval via Regularized Metric Learning
In content-based image retrieval (CBIR), relevant images are identified based on their similarities to query images. Most CBIR algorithms are hindered by the semantic gap between the low-level image features used for computing image similarity and the high-level semantic concepts conveyed in images....
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sg-smu-ink.sis_research-33042018-12-05T06:09:24Z Collaborative Image Retrieval via Regularized Metric Learning SI, Luo JIN, Rong HOI, Steven C. H. LYU, Michael R. In content-based image retrieval (CBIR), relevant images are identified based on their similarities to query images. Most CBIR algorithms are hindered by the semantic gap between the low-level image features used for computing image similarity and the high-level semantic concepts conveyed in images. One way to reduce the semantic gap is to utilize the log data of users' feedback that has been collected by CBIR systems in history, which is also called “collaborative image retrieval.” In this paper, we present a novel metric learning approach, named “regularized metric learning,” for collaborative image retrieval, which learns a distance metric by exploring the correlation between low-level image features and the log data of users' relevance judgments. Compared to the previous research, a regularization mechanism is used in our algorithm to effectively prevent overfitting. Meanwhile, we formulate the proposed learning algorithm into a semidefinite programming problem, which can be solved very efficiently by existing software packages and is scalable to the size of log data. An extensive set of experiments has been conducted to show that the new algorithm can substantially improve the retrieval accuracy of a baseline CBIR system using Euclidean distance metric, even with a modest amount of log data. The experiment also indicates that the new algorithm is more effective and more efficient than two alternative algorithms, which exploit log data for image retrieval. 2006-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2304 info:doi/10.1007/s00530-006-0033-1 https://ink.library.smu.edu.sg/context/sis_research/article/3304/viewcontent/Collaborative_Image_Retrieval_via_Regularized_Metric_Learning.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Content-based image retrieval Relevance feedback Log-based relevance feedback Relevance feedback log Users Semantic gap Metric learning Regularization Semidefinite programming Computer Sciences Databases and Information Systems |
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Content-based image retrieval Relevance feedback Log-based relevance feedback Relevance feedback log Users Semantic gap Metric learning Regularization Semidefinite programming Computer Sciences Databases and Information Systems SI, Luo JIN, Rong HOI, Steven C. H. LYU, Michael R. Collaborative Image Retrieval via Regularized Metric Learning |
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In content-based image retrieval (CBIR), relevant images are identified based on their similarities to query images. Most CBIR algorithms are hindered by the semantic gap between the low-level image features used for computing image similarity and the high-level semantic concepts conveyed in images. One way to reduce the semantic gap is to utilize the log data of users' feedback that has been collected by CBIR systems in history, which is also called “collaborative image retrieval.” In this paper, we present a novel metric learning approach, named “regularized metric learning,” for collaborative image retrieval, which learns a distance metric by exploring the correlation between low-level image features and the log data of users' relevance judgments. Compared to the previous research, a regularization mechanism is used in our algorithm to effectively prevent overfitting. Meanwhile, we formulate the proposed learning algorithm into a semidefinite programming problem, which can be solved very efficiently by existing software packages and is scalable to the size of log data. An extensive set of experiments has been conducted to show that the new algorithm can substantially improve the retrieval accuracy of a baseline CBIR system using Euclidean distance metric, even with a modest amount of log data. The experiment also indicates that the new algorithm is more effective and more efficient than two alternative algorithms, which exploit log data for image retrieval. |
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SI, Luo JIN, Rong HOI, Steven C. H. LYU, Michael R. |
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SI, Luo JIN, Rong HOI, Steven C. H. LYU, Michael R. |
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SI, Luo |
title |
Collaborative Image Retrieval via Regularized Metric Learning |
title_short |
Collaborative Image Retrieval via Regularized Metric Learning |
title_full |
Collaborative Image Retrieval via Regularized Metric Learning |
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Collaborative Image Retrieval via Regularized Metric Learning |
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Collaborative Image Retrieval via Regularized Metric Learning |
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collaborative image retrieval via regularized metric learning |
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Institutional Knowledge at Singapore Management University |
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2006 |
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https://ink.library.smu.edu.sg/sis_research/2304 https://ink.library.smu.edu.sg/context/sis_research/article/3304/viewcontent/Collaborative_Image_Retrieval_via_Regularized_Metric_Learning.pdf |
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